An extension of social network group decision-making based on trustrank and personas

Abstract

With the development of social networking big data, social network group decision-making (SN-GDM) has been widely applied in many fields. This paper focuses on three main components: (1) the determination of the decision makers’ (DMs) weights based on different social influence; (2) the anti-deception mechanism; and (3) the persona method. We introduce the TrustRank algorithm and the persona method into SN-GDM. Based on the TrustRank algorithm, both trusted and deceptive DMs in a seed set are artificially identified and given initial static scores to derive the influence of each DM. Additionally, the persona method is introduced to cluster DMs and achieve personalized decision-making. Further, we present a numerical example and comparison to demonstrate the efficiency of the framework in coping with non-socially shared preferences in SN-GDM. As expected, our findings indicate that our framework reduces the influence of deceptive DMs on the decision results.